A Real-Time Deformable Detector

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Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimoda ...

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Informative sample selection in an active learning (AL) setting helps a machine learning system attain optimum performance with minimum labeled samples, thus reducing annotation costs and boosting performance of computer-aided diagnosis systems in the pres ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Regular inspection and maintenance are crucial for ensuring the optimal performance of solar panels. However, conventional manual methods can be laborious, time consuming, and expensive, especially for large and inaccessible installations. Aerial inspectio ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Students learn more when they are actively engaged in the learning process. While hands-on activities, labs and projects are moments when students are active, the learning benefits can be amplified with coaching strategies. This activity will enable studen ...

Conversational tutoring systems (CTSs) offer a promising avenue for individualized learning support, especially in domains like persuasive writing. Although these systems have the potential to enhance the learning process, the specific role of learner cont ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across similar to 15 000deg(2) of the sky. Euclid is expected to detect similar to 12 billion astronomical source ...

A Real-Time Deformable Detector

Chat with Graph Search

Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

SIMBa: System Identification Methods Leveraging Backpropagation

Reliable data-driven decision-making through optimal transport

Aggregating Spatial and Photometric Context for Photometric Stereo

Boosting likelihood learning with event reweighting

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Drone‐Based Solar Cell Inspection With Autonomous Deep Learning

Social Opinion Formation and Decision Making Under Communication Trends

Understanding generalization and robustness in modern deep learning

Meta-learning to address diverse Earth observation problems across resolutions

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Enabling Uncertainty Estimation in Iterative Neural Networks

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

How to support students to develop coaching and peer teaching skills

Evaluating the Impact of Learner Control and Interactivity in Conversational Tutoring Systems for Persuasive Writing

Robust machine learning for neuroscientific inference

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning